#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim, l2_loss
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
from utils.graphics_utils import cartesian_to_spherical, spherical_to_cartesian, generate_sun_map, projection_ndc
import uuid
from torchvision.utils import save_image
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
try:
    from torch.utils.tensorboard import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False

def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset)
    gaussians = GaussianModel(dataset)
    scene = Scene(dataset, gaussians)
    gaussians.training_setup(opt)
    if checkpoint:
        (model_params, first_iter) = torch.load(checkpoint)
        gaussians.restore(model_params, opt)

    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
    
    viewpoint_stack = None
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    sampled_sun_pos = None
    for iteration in range(first_iter, opt.iterations + 1):        

        gaussians.update_learning_rate(iteration)

        # Every 1000 its we increase the levels of SH up to a maximum degree
        if iteration % 1000 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        if not viewpoint_stack:
            viewpoint_stack = scene.getTrainCameras().copy()
        # viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
        viewpoint_cam = viewpoint_stack[0]
        
        # if sampled_sun_pos is None:
        sampled_sun_pos = gaussians.sample_sun_pos(viewpoint_cam)
        theta, phi = cartesian_to_spherical(sampled_sun_pos, gaussians.sky_center)
        # xyz = spherical_to_cartesian(theta, phi, gaussians.sky_distance, gaussians.sky_center)
        
        # Render
        if (iteration - 1) == debug_from:
            pipe.debug = True

        bg = torch.rand((3), device="cuda") if opt.random_background else background

        density = 50
        render_pkg = render(viewpoint_cam, gaussians, pipe, bg, theta=theta, phi=phi, density=torch.ones_like(theta)*density)
        image = render_pkg["render"]
        sunpos_map = render_pkg["sunpos_map"]
        viewspace_point_tensor = render_pkg["viewspace_points"]
        visibility_filter = render_pkg["visibility_filter"]
        radii = render_pkg["radii"]

        # Loss
        gt_image = viewpoint_cam.original_image.cuda()
        
        gt_sun_pos_2d, _, _ = projection_ndc(sampled_sun_pos[None], viewpoint_cam)
        
        
        # print(gt_sun_pos_2d, viewpoint_cam.image_height, viewpoint_cam.image_width)
        
        gt_sunpos = generate_sun_map(viewpoint_cam.image_height, 
                              viewpoint_cam.image_width,
                              gt_sun_pos_2d[0, 0],
                              gt_sun_pos_2d[0, 1],
                              density,
                              gt_image.device)
        
        loss = l2_loss(sunpos_map, gt_sunpos)
        # loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_sunpos))
        # psnr_ = psnr(gt_sunpos, image).mean()
        loss.backward()
        
        if iteration % 200 == 0:
            save_dir = os.path.join(dataset.model_path, f'train_vis')
            os.makedirs(save_dir, exist_ok=True)
            img = torch.cat([sunpos_map.repeat(3,1,1), gt_sunpos.repeat(3,1,1)], -1)
            save_image(img, os.path.join(save_dir, f"iters_{iteration:04d}.png"))

        with torch.no_grad():
            # Progress bar
            if iteration % 10 == 0:
                progress_bar.set_postfix({"LOSS": f"{loss:.6f}", "Pts": f"{gaussians.get_xyz.shape[0]}"})
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log and save
            # training_report(iteration, l1_loss, testing_iterations, scene, render, (pipe, background))
            if (iteration in saving_iterations):
                print("\n[ITER {}] Saving Gaussians".format(iteration))
                scene.save(iteration)

            # Densification
            # if iteration < opt.densify_until_iter:
            #     # Keep track of max radii in image-space for pruning
            #     gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
            #     gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)

            #     if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
            #         size_threshold = 20 if iteration > opt.opacity_reset_interval else None
            #         gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
                
            #     if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
            #         gaussians.reset_opacity()

            # Optimizer step
            if iteration < opt.iterations:
                gaussians.optimizer.step()
                gaussians.optimizer.zero_grad(set_to_none = True)

            if (iteration in checkpoint_iterations):
                print("\n[ITER {}] Saving Checkpoint".format(iteration))
                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")

def prepare_output_and_logger(args):    
    if not args.model_path:
        args.model_path = os.path.join("./output/", args.source_path.split('/')[-1])
        
    # Set up output folder
    print("Output folder: {}".format(args.model_path))
    os.makedirs(args.model_path, exist_ok = True)
    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
        cfg_log_f.write(str(Namespace(**vars(args))))

    # Create Tensorboard writer
    tb_writer = None
    if TENSORBOARD_FOUND:
        tb_writer = SummaryWriter(args.model_path)
    else:
        print("Tensorboard not available: not logging progress")
    return tb_writer

def training_report(iteration, l1_loss, testing_iterations, scene : Scene, renderFunc, renderArgs):

    # Report test and samples of training set
    if iteration in testing_iterations:
        torch.cuda.empty_cache()
        validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()}, 
                              {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})

        for config in validation_configs:
            if config['cameras'] and len(config['cameras']) > 0:
                l1_test = 0.0
                psnr_test = 0.0
                for idx, viewpoint in enumerate(config['cameras']):
                    image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0)
                    gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
                    l1_test += l1_loss(image, gt_image).mean().double()
                    psnr_test += psnr(image, gt_image).mean().double()
                psnr_test /= len(config['cameras'])
                l1_test /= len(config['cameras'])          
                print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
        torch.cuda.empty_cache()

if __name__ == "__main__":
    # Set up command line argument parser
    parser = ArgumentParser(description="Training script parameters")
    lp = ModelParams(parser)
    op = OptimizationParams(parser)
    pp = PipelineParams(parser)
    parser.add_argument('--ip', type=str, default="127.0.0.1")
    parser.add_argument('--port', type=int, default=6009)
    parser.add_argument('--debug_from', type=int, default=-1)
    parser.add_argument('--detect_anomaly', action='store_true', default=False)
    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[30_000])
    parser.add_argument("--quiet", action="store_true")
    parser.add_argument("--gpu", type=int, default=0)
    parser.add_argument("--ratio", nargs='+', type=float)
    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
    parser.add_argument("--start_checkpoint", type=str, default = None)
    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)
    
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)
    torch.cuda.set_device(args.gpu)

    # Start GUI server, configure and run training
    # network_gui.init(args.ip, args.port)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)

    # All done
    print("\nTraining complete.")
